# apache/pinot

Apache Pinot - A realtime distributed OLAP datastore

Repository: https://github.com/apache/pinot
Canonical: https://ross.abutalabs.com/products/pinot
Homepage: https://pinot.apache.org/
Language: Java
License: Apache-2.0
License Family: permissive
Topics: java
Last push: 2026-08-26T23:30:10+00:00

## Health v2 (maintenance only)
Score: 84/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 55, longevity 100
- inputs: {"age_days": 4489, "days_push": 7, "days_rel": 90, "gap_med": 205, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6128, forks 1502 (observed 2026-08-28T04:09:36.305564+00:00)

## What it is
Apache Pinot is an open-source distributed OLAP datastore purpose-built for low-latency, high-throughput real-time analytics. It ingests data from streams like Kafka, Pulsar, and Kinesis (or batch sources) and serves sub-second SQL queries at very high concurrency, supporting both user-facing dashboards and AI agent workloads.

## Use cases
- serve sub-second analytics queries for user-facing dashboards
- ingest streaming data from Kafka and query it within seconds
- build embedded analytics APIs with high concurrency
- power real-time leaderboards and metrics endpoints
- provide fresh data retrieval for RAG and LLM agents
- run multi-tenant customer-facing analytics
- feed live signals to fraud detection or bidding engines

## When to choose
- you need sub-second (P99 <100ms) queries on fresh streaming data at petabyte scale
- your application serves analytics to many concurrent end users or AI agents
- you need real-time ingestion from Kafka, Pulsar, or Kinesis with immediate queryability
- you want hybrid real-time plus offline tables with upsert/CDC support

## When to avoid
- you need complex transactional (OLTP) workloads with joins and updates
- your data is small enough for a single Postgres or MySQL instance
- you only run nightly batch reporting where freshness and latency don't matter
- you lack the operational capacity to run a distributed multi-node cluster

## Facets
- artifact type: service
- maturity: stable
- function: database, search-engine, analytics, streaming, etl
- domain: databases, analytics, big-data
- platform: jvm, cloud, self-hosted
- tags: olap, real-time-analytics, distributed-database, sql, user-facing-analytics, columnar-store, apache, streaming-ingestion, vector-search, real-time, data-engineering, docker, kubernetes

## Member repositories
- apache/pinot (main) score 84

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:36.305564+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:47:58.710767+00:00, confidence not recorded.
  - readme: https://github.com/apache/pinot (fetched 2026-08-28T04:09:36.305564+00:00, sha caffa4885538)
  - homepage: https://pinot.apache.org/ (fetched 2026-08-29T08:44:37.224357+00:00, sha da36dba3f441)
  - site_page: https://docs.pinot.apache.org (fetched 2026-08-29T08:44:37.227020+00:00, sha 41081987a8f2)
  - site_page: https://docs.pinot.apache.org/basics/getting-started (fetched 2026-08-29T08:44:37.228957+00:00, sha 2778dc81c71e)
  - site_page: https://docs.pinot.apache.org/ (fetched 2026-08-29T08:44:37.233961+00:00, sha 41081987a8f2)
  - site_page: https://pinot.apache.org/agent-facing-analytics (fetched 2026-08-29T08:44:37.230613+00:00, sha 92d7c2d223a8)
- Data as of 2026-08-30T08:39:29.467469+00:00.